AAIC Description

8/10/2019 AAIC Description

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Applied Analysis Image Calibrator (AAI C): Automatic Retrieval of Ground

Reflectance from Spectral Imagery

R.L. Huguenin, M.S. Bouchard, C.A. Penney, E.A. Conlon, and G.R. Waddington

Applied Analysis Inc., Westford, MA

November 2013

Abstract

A spectral image processing application,AAIC,is described that automatically transformsspectral imagery from its native digital number (DN) units directly to pixel reflectance

units. The process uses a physics-constrained image statistics approach that is entirelyscene-based and requires no user inputs. Pixel reflectance values are nominally within

0.05 reflectance units of the actual ground directional spectral reflectance value (absolute

band-averaged difference), based on tests with diverse images having targets with known

field-measured reflectance spectra. A Calibration Confidence metric is automaticallygenerated, estimating the probability that retrieved pixel reflectance values will be within

the nominal accuracy range. The consistency of accuracy performance from image toimage makes possible autonomous application and scene-to-scene reuse of spectral

signatures and indices to detect specific materials of interest and monitor changes.

Introduction

We describe here a spectral image processing application,AAIC, for automaticallytransforming spectral imagery from its native digital number (DN) units to units of

ground material reflectance. The process is a stand-alone application that is fully

automatic, requiring no user interaction. Only the image file name is required as user

input for most images. The sensor and data type are automatically retrieved from theimage header, when available, while all the other needed information is derived

automatically from the scene data alone.

The image-statistics approach eliminates dependence on user knowledge and

judgment to correctly estimate input parameters and adjust them in accordance with

scene-to-scene variations in environmental and acquisition conditions. As such, it notonly significantly reduces the levels of user skill and experience normally required to

achieve the accuracies of physics-constrained atmospheric correction models. It also

reduces the potential for user errors, and enhances scene-to-scene uniformity of accuracy


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performance. Furthermore, since the results for any given scene are fully repeatable, it

allows investigators to reproduce or meaningfully extend the results reported previously

or by others for that scene.

Algorithm description

AAICuses a physics-constrained image statistics approach, i.e., it employs algorithms

that are fully traceable to standard atmospheric radiation transfer and sensor

phenomenology in the reflective solar wavelength regime (350-2500nm). Two scene-

average n-band calibration spectra, ACF(n) and SCF(n), are derived from the image data.These calibration spectra relate the pixel Digital Number values for each spectral band,

DN(n), to ground reflectance, Ref(n), according to the expression

DN(n) = ACF(n) + SCF(n)*Ref(n) (1)

DN(n) corresponds to the reported pixel intensity value in the raw Digital Number image

in spectral band n. The two terms on the right hand side of the expression correspond tothe two principal radiance components that comprise the reported DN(n) spectrum for a

cloud-free pixel. The first term, ACF(n), corresponds to the scene-average

atmospherically scattered solar radiance component, expressed in DN units. The secondterm, SCF(n)*Ref(n), represents the ground radiance component expressed in DN units.

The ACF(n) component represents the sum of the atmospherically scattered solar

radiation, A(n), and sensor spectral transfer function offset, O(n), contributions to image

pixel spectra:

ACF(n) = A(n) + O(n) (2)

O(n) corresponds to the DN(n) value for a sensor dark field (no incoming radiance). A(n)

includes the scene-average atmospherically backscattered component of incident solar

radiation, expressed in DN units. ACF(n) can be viewed as simulating the pixel spectrumof a deployed black calibration panel (no ground contribution to the pixel spectrum).

The second term in Expression 1 is a product of two terms, SCF(n) and the desired

ground directional spectral reflectance term, Ref(n). SCF(n) represents the product of thesensor spectral transfer function gain factors, G(n), and the scene-average spectrum of the

two-path atmospherically attenuated incident and reflected solar radiance, S(n):

SCF(n) = G(n)S(n) (3)

G(n) corresponds to the DN(n) value for a sensor flat field minus the dark field value,

O(n). S(n) represents the scene-average integrated two-path atmospheric attenuation ofthe incident and emerging (reflected in the direction of the sensor) solar radiance. SCF(n)


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then selected. From these four spectra, the Phase I approximations of ACF(n) and SCF(n)

for the image of interest are calculated.

The Phase I process makes the assumption that the relatively bright matched pair of

DN(n) spectra have common reflectance spectra, i.e., RefhA= RefhB. Similarly, for therelatively dark matched pair, the process assumes RefdA= RefdB. Here A and B refer tothe reference image and the image of interest, respectively, and b and d refer to the

relatively bright and relatively dark background reference spectra, respectively. SCFAand

ACFA(reference image) are known accurately, while SCFBand ACFB(image of interest)are the unknowns solved for. Following this initial (Phase 1) estimate, ACFB(n) and

SCFB(n) are progressively iterated using refined matches with a large number of matched

pairs and a progression based on the ARAD(n) and SUN(n) generated by each prior

iteration. The iterations continue until ACFB(n) and SCFB(n) reach convergence, yielding

an intermediate (Phase II) estimate of ACF(n) and SCF(n) for the image of interest.

The final (Phase III) estimate of ACF(n) and SCF(n) for the image of interest usesan automated subpixel process. The Phase III process uses a set of probe signatures

derived from the reference images. These signatures are derived from multiple referencescenes, containing a broad cross-section of land cover materials. The probe signatures are

transformations of the reference image ground cover spectra, enabling the detection of

subpixel occurrences of those materials in the image of interest. The corresponding

signatures in the image of interest are derived automatically using a process based on theAdaptive Signature Kernel(ASK) application (Huguenin et al., 1998).ASKderives a new

child signature from subpixel detections of the probe signature in the image of interest.

It does this by clustering the detected subpixel occurrences according to spectralsimilarity using an approach modeled after that employed by ISODATA, and it computes

means and standard deviations of the clusters. Using criteria based on the populations and

standard deviations of the clusters, the best cluster is identified and its mean spectrum

becomes the child signature.ASKthen artificially inserts the signature into the imageover a range of subpixel fractions and background types, and the detections of these

dopedoccurrences are used to derive the signature tolerances. Sets of matched probe-

child signatures are then used to solve for ACFB(n) and SCFB(n) in the same way as for

Phases I and II.

This Phase III subpixel step can significantly improve accuracy and image-to-imagerobustness over that of the Phase II whole pixel step. This is due in part to the fact that

the ground cover spectra represented by the probe signatures typically correspond to

naturally associated mixtures of groundcovers, rather than pure materials. The spectra of

these naturally associated mixtures can vary from pixel to pixel and image to image dueto natural variations in the relative fractions of constituent components present in the

mixtures. These variations can cause the pixel spectra to potentially significantly deviate

from the probe signature spectrum. The subpixel step is able to retrieve the spectrum ofthe particular set of constituent fractions to which the probe signature corresponds by

identifying and suppressing the spectral contributions from the excess constituent


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fractions in each pixel. It also effectively suppresses differences introduced by regional

variations in the inherent spectral properties of the individual constituents of the mixture(e.g., oaks versus maple). This latter effect contributes significantly to the observed

robustness of the process across a broad diversity of geographical and environmental

settings. Finally, the subpixel step identifies and suppresses contributions fromextraneous background components in the pixel that can frequently mask the signature ofthe target groundcover material. The net result is a significantly improved set of spectral

matches to the signature spectra across scenes.

Errors in calculations of ACF(n) and SCF(n) due to scene-to-scene material

differences between the probe and child signatures are suppressed through 1) clustering

of the retrieved ACF(n) and SCF(n); 2) identifying the best cluster (by population,diversity of source signatures in the cluster, and low standard deviation); and 3)

averaging the derived ACF(n) and SCF(n) values within the cluster. The best error

suppression and adherence to the underlying phenomenology occur when clusters contain

ACF(n) and SCF(n) values that are common to probe-child signature pairs acrossmultiple signature materials.

After completing Phase III, the process reports an internally generated performance

metric that serves as a first order estimate of image quality and calibration accuracy. TheCalibration Confidence metric is based on the spectral similarity (absolute mean spectraldifference) of the specific probe signatures, Probep(n), used for calculating ACF(n) and

SCF(n) to the child signatures, Childp(n), generated by those probe signatures. Here p is

the probe signature identifier and n is the spectral band number. With Probep(n) and

Childp(n) in reflectance units,

Calibration Confidence = (1.0meanDiff) x 100%, (5)

meanDiff = pabsDiffp/ P

absDiffp= abs [ n(Probep(n)Childp(n))/N ]

P = number of probe signatures, N = number of spectral bands

Calibration Confidence is a measure of the goodness of the spectral match of the

probe materials to the actual materials in the image of interest, thereby providing an

estimate of the likelihood that materials spectrally similar to the probe materials wereindeed in the image and that an accurate calibration could be generated. As long as there

are at least three good matches between the probe and scene materials, the process wasdesigned to be able to accurately retrieve ACF(n) and SCF(n) even in scenes with a lowdiversity of cover characteristics.

Although the approach is relatively insensitive to image-to-image variations in

scene content, not all images would be expected to produce high accuracy calibrations.

Images with temporal artifacts (discussed below), atmospheric dust palls, non-uniform


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haze, or other image quality aberrations can sometimes cause spectral matches between

the probe and image materials to degrade to the point that calibration accuracies can dropbelow expected levels. Although these aberrations do not necessarily lead to poor

calibrations, they can significantly lower the probability of achieving an accurate

calibration. As discussed below, data suggest that images with significant qualityproblems might be expected to report Calibration Confidence values below a nominalthreshold value. Accepting only those images whose Calibration Confidence values

exceed the threshold provides a capability for insuring that those images were of high

enough quality that the derivation of ACF(n) and SCF(n) properly adhered to theunderlying phenomenology throughout the processing sequence.

An illustration of calibration results for an airborne AVIRIS hyperspectral image of

the Stennis Space Center (SSC), MS is shown in Figures 1 - 4. In Figure 1 is shown the

average DN(n) spectrum (184 bands) of the image pixels comprising a small grassy area

of interest (AOI) selected by NASA as one of several field data sites in the SSC TargetField (Holekamp, 2004). In Figure 2 are shown the calibration spectra, ACF(n) and

SCF(n), retrieved by AAICfrom the image, and used to automatically transform theimage to units of reflectance (Equation 4). In Figure 3 is shown the resultant averageRef(n) spectrum (184 bands) of the grassy AOI, retrieved from the derived reflectance

image.

Comparison of the spectrum in Figure 3 to the one in Figure 1 reveals the extent of

the transformation from DN(n) to Ref(n). Also shown in Figure 3 is the field-measuredreflectance spectrum (labeled GT) provided by NASA for this AOI. Comparison of the

two spectra reveals good general agreement, with the greatest differences occurring in the

800-1500nm region. The latter differences are likely due at least in part to the non-

uniform cover characteristics and different coverage included in the larger image AOI

versus those of the field measurements. Even with these differences, however, the mean(band-averaged) absolute difference between the two spectra is only .01376 reflectance

units (0.01.0 reflectance scale), and the correlation coefficient (0.9978) between thetwo spectra is quite high. The reported Calibration Confidence value for the image was

88.5%.


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Figure 1. Average DN(n) spectrum (184 bands) of the image pixels comprising a grassy

area of interest (AOI) designated by NASA as one of several field data sites in theAVIRIS image cube of the Stennis Space Center, MS, acquired on 10/27/1998.

Figure 2. ACF(n) (left) and SCF(n) (right) spectra automatically retrieved by AAICfrom

the AVIRIS image cube of the Stennis Space Center, MS, acquired on 10/27/1998.

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Figure 3. Comparison of the image-derived and field-measured reflectance spectra of thesame grassy AOI for which the raw data spectrum is shown in Figure 1. The image-

derived spectrum is the dotted curve, and the field-measured spectrum is the solid

spectrum.

Quantitative Assessment of Reflectance Accuracy

Quantitative accuracy of the reflectance images produced by AAICwas assessed using asuite of images containing deployed panels and other ground materials having known

field-measured spectra. In addition to the AVIRIS hyperspectral image of Stennis Space

Center, MS discussed above, were four HYDICE hyperspectral images (Desert Radiance

II Runs 03 and 13 of the Yuma Proving Ground, AZ , and Littoral Radiance II Runs 28and 47 of Red Beach and Camp Pendleton, CA, respectively); IKONOS and QuickBird

4-band multispectral images of the Stennis Space Center Target Field; and a RapidEye 5-

band multispectral image of the Railroad Valley Playa Test Site, NV. The HYDICEimages each contained 4-7 deployed reflectance calibration panels spanning from low

reflectance values (.02-.04) to high reflectance values (.60-.64). The AVIRIS, IKONOS,and QuickBird images contained a variety of background materials, and the RapidEyeimage contained two natural playa areas at the Railroad Valley site. The images spanned

a relatively wide range of representative land cover conditions. The accuracy was

measured in terms of spectral mean absolute reflectance difference (average across

spectral bands) between the image-derived and field measured spectra for each panel orground material in the image. The difference values for the suite of panels or background


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materials in an image were then averaged to produce a mean absolute reflectance

difference value for the image.

Note that the results in Table 1 for the RapidEye Railroad valley Playa image werenot based on use of the full image for calibration. We used a subset that included only

the southern half of the image to retrieve ACF(n) and SCF(n), which were then applied

to the entire DN(n) image to obtain the Ref(n) image using Equation 4. This was done toavoid anomalous temporal spectral artifacts in the northern part of the image, produced

as a consequence of RapidEyes focal plane configuration. The images acquired in the

different spectral bands by RapidEye are spatially offset in the focal plane, causing them

to be temporally offset from one another. Pixels containing scene features that weremoving relative to the background terrain at the time of image acquisition were not

properly corrected for sensor motion, which adversely impacted their spectral integrity

by superimposing spectral band values from multiple locations. The sources of these

temporal spectral artifacts include moving atmospheric features, such as clouds, plumes,flying aircraft, and contrails; moving aquatic features, such as waves and boats; and

dynamic land features, such as moving vehicles. There were enough moving cloudpixels in the northern part of the Railroad Valley Playa image to corrupt the calibration,so the calibration was based instead on the subset (southern half) containing no clouds.

A similar temporal spectral artifact problem can arise with eight-band

WorldView2 imagery. This sensor contains two sets of detector arrays in the focal plane.

One set is the same as used for the four-band WorldView2 imagery. The second set isspatially offset from the first in a manner that leads to anomalous temporal spectral

artifacts that are similar to the ones produced by the RapidEye sensor. The AAIC

algorithm was specially modified to handle the temporal offset in the eight-band

WorldView2 imagery. The two WorldView2 image band sets are processed

independently, and the two reflectance images are merged into a single eight-bandreflectance image to compensate for the temporal offset. For this manuscript, we were

unable to locate an eight-band WorldView2 image of an area containing areas withcompanion field-measured spectra. To illustrate the algorithmic approach used by AAIC,

images were simulated for both a four-band and an eight-band configuration using

subsetted band sets from the HYDICE hyperspectral Run 47 image at the published

WorldView2 band passes. AAIC was used to process both images, and we calculated thespectral mean absolute reflectance differences for the set of panels in each. The resultant

difference and Calibration Confidence values are shown in Table 1. Although AAIC

reports both Calibration Confidence values for the two four-band subsets of the eight-band WorldView2 imagery, we report the simple average of the two confidence values in

Table 1.The mean absolute difference values for the 14 images in Table 1 ranged from

.01376 to .05050, with a mean of .02916 and standard deviation of .00948. Although thesample set is relatively small, the values are relatively uniformly distributed about the

mean. This suggests that the pixel spectral mean (band averaged) absolute reflectance

values for ~95% (mean 2 standard deviations) of images with comparable Calibration


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Confidence values would be expected to be within 0.05 reflectance units of the actual

ground spectral mean reflectance.

The corresponding Calibration Confidence values for the images in Table 1 had amean and standard deviation of 89.26% and 4.19%, respectively. The correlation

coefficient between the errors and Calibration Confidence values was only 0.4089,

however, confirming that Calibration Confidence is not an effective predictor of relativescene-to-scene accuracy. Rather, it serves only as a predictor of the probability that the

accuracy standard (spectral mean calculated reflectance within 0.05 of actual mean

reflectance) was met.

Table 1.

Sensor Location Bands *Difference Cal. Conf.

AVIRIS Stennis Space Center, MS 184 .01376 88.55

HYDICE Yuma Proving Ground, AZ, Run 03 180 .01699 81.41

HYDICE Yuma Proving Ground, AZ, Run 13 180 .03669 83.67

HYDICE Red Beach, CA, Run 28 156 .02749 85.93

HYDICE Camp Pendleton, CA, Run 47 180 .03079 83.88

Landsat TM5 Railroad Valley Playa, NV (6/03/00) 6 .02679 94.00



**RapidEye Railroad Valley Playa, NV 5 .02805 88.67

QuickBird Stennis Space Center, MS 4 .01786 89.05

IKONOS Stennis Space Center, MS 4 .03603 90.61

WorldView2 (4-band) Railroad Valley Playa, NV 4 .05050 94.89

***WorldView2 (4-band) Simulated from Run 47 4 .03404 91.75

***WorldView2 (8-band) Simulated from Run 47 8 .03140 92.30

Mean .02916 89.26

Standard Deviation .00948 4.19

As discussed above, to achieve the accuracy standard, image quality needs to behigh enough that at least some good matches will occur between the probe and scene

materials so that the process can retrieve accurate ACF(n) and SCF(n) values. Since

Calibration Confidence is an indicator of the quality of the matches, this suggests thatthere may be a threshold for Calibration Confidence below which it is unlikely that the

accuracy standard can be met. As for the mean absolute reflectance difference values, the

Calibration Confidence values in Table 1 are relatively evenly spread about the mean,


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suggesting that a nominal operating range for Calibration Confidence can be estimated

based on the mean and standard deviation, namely 80.997.6% (mean 2 standarddeviations) for 95% of the images. Outside of this range, there is an increased likelihood

that image quality is inadequate to achieve a reliably convergent estimate of ACF(n)

and/or SCF(n) and meet the accuracy standard. This is supported by numerousobservations (see below), indicating that Calibration Confidence can serve as an effectiveimage quality screen. In particular, by accepting only images having Calibration

Confidence values above a threshold value of 80%, the likelihood significantly increases

that the image-retrieved reflectance values will meet the nominal accuracy standard.

Comparisons of the image-derived and field-measured spectra of the referencematerials for several of the images listed in Table 1 are shown in Figures 4-7 for

illustration. These plots provide examples of the kinds of spectral errors that are

represented by the mean absolute reflectance difference values in Table 1. In Figure 4 are

shown plots of the image-derived versus field measured spectra for the five deployedreflectance reference panels in the 26 June 1995 HYDICE hyperspectral image of a site

in the Castle Dome area of the US Army Yuma Proving Ground in Arizona (Run 03,FootballField Target Array, HYMSMO Desert Radiance II data collect). The labelsrefer to AAIC-derived and field-measured spectra for the 2%, 12%, 24%, 36% and 48%

reflectance reference panels. The image-retrieved spectra are averages of pixel

reflectance spectra for areas of interest (AOI) within the interiors of the panels, excluding

mixed pixels along the panel edges. The field spectra were measured by the US ArmyTopographic Engineering Center using a GER Field Spectroradiometer (350-2500nm) in

a nadir viewing position on axis with the sun (Evans et al., 1995). Field spectra were

collected at three different sample areas within each panel, averaged, and spectralreflectance was computed relative to a Spectralon reflectance standard. In general there

was good agreement between the image-retrieved and field-measured spectra with respect

to both spectral shape and intensity. The differences for the 36% panel appear anomalous,however, suggesting that the average field spectrum for that panel may be less

representative than for the other panels. The spectral absolute mean reflectance difference

value listed for this image in Table 1 was relatively low (.01699), even though theCalibration Confidence value (81.41%) was close to the nominal threshold value.


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Figure 4. Comparison of the image-derived and field-measured reflectance spectra of five

reflectance calibration panels deployed during Run03 of the Desert Radiance II exercise

at the Yuma Proving Ground on 26 June, 1995. The airborne HYDICE hyperspectral

image-derived spectra are shown as dotted curves, and the field-measured spectra are

shown as solid curves.

In Figure 5 are shown the results for the 5-band RapidEye image of the Railroad

Valley Playa, NV site. Here the material of interest is a natural playa deposit. Althoughfield-measured reflectance spectra were acquired at the time of overpass (Naughton et al,

2011), they were not available for the present study. As a substitute we used the vicarious

measurements acquired by the University of Arizona Optical Sciences Center (7 June and

10 June 2000). The image was acquired on 15 October, 2009, which was a differentseason and year, and the playa had likely undergone significant changes over the

intervening interval. Thome(2001)described the test site and described separately

(Thome, private communication, 2012) its history of spectral variability with changing

environmental conditions (standing water, erosional redistribution of salts, soil moisturelevels, etc.). Consequently, comparison of the image-retrieved pixel spectra to vicarious

field-measured spectra as a means of quantitatively assessing calibration accuracy at the

Railroad Valley playa and similar sites needs to be done with extraordinary caution. Theexpected differences in soil conditions can explain much of the spectral difference

between spectra in Figure 5. The spectral mean absolute reflectance difference (.02805)

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and Calibration Confidence value (88.67%) were both relatively close to the mean of the

values in Table 1, however.

In Figure 6 are shown the results for another image of the Railroad Valley Playasite, this one measured by the WorldView2 (4-band) sensor. This image had a

comparatively large reported spectral mean absolute difference (.05050) relative to the

field measurement, even though it had a relatively high Calibration Confidence value(94.59%). The flattening and higher overall reflectance of the image-retrieved spectrum

relative to the field-measured spectrum is consistent with the expected differences in

playa conditions (dryness and increased amount of exposed salt) on the image date (21

October, 2011) versus the field measurement date (10 June, 2000).

Figure 5. Comparison of the image-retrieved and field-measured reflectance spectra of

the Railroad Valley Playa, NV, measured by the commercial 5-band RapidEye satellitesensor on 15 October, 2009

In Figure 7 are shown the image-retrieved versus field-measured spectra for the

same grassy AOI shown in Figure 2, but this time from a 4-band QuickBird image of the

Stennis Space Center test site, acquired on 10 January 2004. Note that the reportedspectral mean absolute reflectance difference (.01786) and Calibration Confidence

(90.61%) are generally similar to the AVIRIS hyperspectral image values for this site,

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even though the sensor and platform were very different and the grass is susceptible to

seasonal variability.

Figure 6. Comparison of the image-derived and field-measured reflectance spectra of the

Railroad Valley Playa, NV, measured by the commercial 4-band WorldView2 satellite

sensor on 21 October, 2011.

Figure 7. Comparison of the image-derived and field-measured reflectance spectra of the

Stennis Space Center, MS test site measured by the commercial 4-band QuickBird

satellite sensor on 10 January, 2004

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Figure 9. Arial (left) and ground (right) photographs of the blue tarps at the LynnhavenBay, VA construction site. The photos were acquired a few days prior to the image in

Figure 9 (photos courtesy of Google Earth).

To illustrate the stability of the process between images having varying scene

content for, we applied the same signature to a second image measured by the same

sensor only one second after the image in Figure 8, and it included the identical set of

blue tarp targets. The two images are shown in Figure 10. The yellow arrows indicate thelocation of the construction site. The scene content of the earlier image (left) is

dominated by ocean, while the later one (right) it is dominated by land with different landcover content than the one on the left. Furthermore, the land portion in the earlier imageis dominantly in a coastal setting, while in the later image the land portion had more

inland content. These differences were manifested in the differences in the derived AAIC

correction spectra shown in Table 2. The extent of scattering (ACF) was somewhat

higher in the earlier image, while the path attenuation fraction (SCF) was lower,consistent with the higher atmospheric aerosol content in the coastal versus inland

environments for the land portions of the two images sampled by AAIC. The Calibration

Confidence values were essentially the same. In Figure 11 we compare the blue tarpdetection results for the two images using the same signature, same difference metric

(Spectral Angle Mapper), and same signature tolerance value (0.30). The detection results

were essentially unchanged between the two images, illustrating the stability of theprocess under these varying scene content conditions used byAAICfor deriving the

transformation to reflectance.


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Figure 10. WorldView2 images containing the Lynnhaven Bay, VA construction site

(yellow arrow). The image on the left was acquired on 9 January 2011 at 15:57:39 GMT,

and the image on the right was acquired one second later.

Table 2

Band 1 Band 2 Band 3 Band 4

Earlier Image

ACF = 261.695 192.800 42.606 54.297

SCF = 2436.213 3609.275 2335.121 2410.562

Later Image

ACF = 244.424 153.838 37.187 51.386

SCF = 3670.382 4897.893 2630.227 2022.749


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Figure 11. The blue tarp detection results, highlighted in green, for the WorldView2

image of Lynnhaven Bay, VA acquired 9 Jan 2011 at 15:57:39GMT (Figure 11 left) areshown on the left. The results for the companion WorldView2 image acquired one second

later (Figure 11 right) are shown on the right.

We next processed a third image of the same construction site, this time measuredby a different sensor (GeoEye) and several months later (12 May 2011). By this date

much of the construction that was underway in January had been completed, and only a

few small tarps remained in the north corner of the site. The same blue tarp signature andSpectral Angle Mapper distance metric was used, but this time the signature toleranceneeded to be increased to 0.38 to detect the tarps in the north corner. The detection results

(highlighted in green) are shown in Figure 12 (left). The yellow arrow indicates the

location of the known tarps, and the viewport in the right image confirms that it was thepersistent tarps that were detected.Other apparent tarp occurrences were detected in thescene, including two new ones on the roof of the newly erected structure at the

construction site and several in the surrounding area associated with recreational pools.

There are several possible reasons why a larger signature tolerance was required to detectthe persistent tarps at the construction site in this image, including a possible error in

reflectance accuracy produced by AAICor a change in the signature properties of the tarp

due to aging and/or development of a coating over time from dust or mold in the

construction site environment, among others.Although there may possibly have been anerror in reflectance accuracy, the reported Calibration Confidence value was relatively

high (94.3%) and comparable to those of the other images. Furthermore, features detected

at a tolerance of 0.30 had the same characteristic blue visual color as the tarps in theearlier images, and were recently deployed (not present in the earlier image). Although

this supports the possibility that weathering may have been responsible for the higher

required signature tolerance, it is currently unknown why the tolerance needed to be

increased in this particular image.


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Figure 12. Blue tarp detection results (left) for a GeoEye image of the Lynnhaven Bay,

VA construction site acquired on 12 May 2011, approximately five months after theimage in Figure 11. The portal (right) confirms the detection of blue tarps.

To further illustrate the scene-to-scene and sensor-to-sensor portability of the

signature enabled by AAICwe repeated the exercise with a series of QuickBird images

measured of the Gulfport, MS area following Hurricane Katrina. In Figure 13 (left) we

show the signature-based detection results for a scene acquired on 6 September 2005,only a week after the 29 August 2005 landfall. The same library signature, Spectral Angle

Mapper distance metric, and signature tolerance (0.30) as used for the Lynnhaven Bay

construction site were used for this image, and comparable detection accuracies wereachieved based on visual comparison to the image using the portal utility (Figure 14,

right). For the most part, the detections represented tarps deployed on damaged roofs.

Two additional QuickBird images of the Gulfport, MS area were acquired on the

same date five weeks later on 12 October 2005, each covering a different part of the city.

Again the same signature and tolerance values were used in both. Although there was asignificantly greater number of detected tarps in these two images than in the earlier

image, the detection accuracies of all three images appeared to be comparable based on a

detailed survey of the images using the Porthole utility. In Figure 14 we show a

comparison of the blue-tarp detection results for an area of Gulfport on the two dates,illustrating the potential for quantitative multi-date change analysis enabled by the scene-

to-scene accuracy consistency of AAIC.


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Figure 13. Detection of blue tarps in an image of Gulfport, MS acquired by the

QuickBird sensor on 6 September 2005 (left). The portal confirmed the detection of bluetarps (right).

Figure 14. Multi-date comparison of blue tarp detections for Ward 2C of Gulfport, MS

(yellow boxes). The QuickBird image on the left was acquired only a week after landfallon 6 September 2005, and the QuickBird image on the right was acquired five weeks

later on 12 October 2005.

Comparison to other processes.

As an autonomous in-scene derivation process that is physics-constrained, AAIC uses a

fundamentally different approach to atmospheric correction than those of other available


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applications. Many of the other applications use predictive physics-based radiation

transfer models rather than in-scene data, and they require user estimation of inputparameters rather than operating autonomously. They include such applications as

ATCOR (Richter, 2011, 2012), which is currently available in Intergraphs ERDAS

IMAGINE; FLAASH (Adler-Golden, et al.,1999), available in ITT Exelis ENVI; and thestand-alone ACORN (AIG, 2001) and ATREM (Gao and Goetz, 1990; Gao et al., 1993;and CSES, 1999) applications. They are largely modeled after the US Air Force-

developed MODTRAN application (Berk et al., 1999), with which pixels are transformed

to units of spectral radiance rather than reflectance. The transformation is based on userestimates of key environmental and encounter geometry parameters for the scene being

processed. The primary differences between them are the methods of parameter

estimation and user interface.

One physics-constrained application that is automated and uses an image statistics-

based approach is CORENV,an atmospheric correction processincluded as an integral

component of the commercially availableERDAS IMAGINE Subpixel Classifierapplication (Huguenin et al., 1997, 1998). While CORENVuses a similar physics-

constrained scene-based algorithmic approach to that of AAIC, it differs in that it wasdesigned specifically to optimize image-to-image portability of scene-derived spectral

signatures rather than performing an in-scene correction to reflectance. Toward this end

CORENVemploys a normalization step not included in AAICto optimize the statistical

spectral match between the signature-source scene and a second scene of interest.

An application that is automated and image statistics-based is QUAC(Quick

Atmospheric Correction), which is available as an optional module in ITT ExelisENVI(Bernstein et al., 2006, 2008). QUACuses an approach that is not physics-constrained,

but rather emulates theEmpirical Line Method (ELM)(Roberts et al., 1985; Conel et al.,

1987) with added normalization. LikeELM, QUACderives an offset and gain spectrumto apply to the image pixels to transform them to spectral reflectance units. UnlikeELM,

however, it does not start with known materials. Instead, QUACfirst derives a

background spectrumfrom the darkest pixels in each band (e.g., from cloud shadows,dark water, or vegetation). It subtracts this spectrum from the pixel spectra, and then

derives a gain spectrum using a set of end member spectra. The end member spectra are

selected to maximize diversity within each spectral band. A normalization step is then

employed, so that a gain spectrum can be derived through regression with a referencelibrary of reflectance spectra, treated as an artificial reference scene. The resultant gain

spectrum is then applied to the image pixels (with the background spectrum subtracted)

to convert them to spectral reflectance units.

Functionally, QUAC shares some similarity to the AAICprocess described here.Its image-statistics approach, however, is not physics-constrained. These differences

introduce sensitivities to scene conditions that can significantly impact the robustness and

uniformity of accuracy performance. One of these sensitivities is to land cover diversity.

In particular, to derive its gain spectrum QUACselects end member spectra from theimage and applies a normalization that assumes that the level of land cover diversity in


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the image being processed is comparable to that of QUACsmaster reference library.

Deviations from the assumed land cover characteristics can significantly impact thenormalization and resultant accuracy of the derived gain factor (Bernstein et al., 2006,

2008). The impact is particularly evident, for example, in images dominated by desert

terrain, densely forested terrain, water, and other low-diversity terrains. This was pointedout and discussed by Bitelli and Mandanici (2010), who processed a hyperspectral imageof a low-diversity conservation area located in the Fayyum Oasis, Egypt. They reported

that QUACproduced pixel reflectance spectra that were significantly suppressed at

visible wavelengths and contained anomalous spikes in the near infrared.

Another sensitivity of the QUACapproach is to the inclusion of physically

unrepresentative dark pixels in the calculation of its background (offset) spectrum. Thebackground spectrum is subtracted from the image under the assumption that it is

representative of an additive offset component that is common to all the pixels. As

discussed above, the common additive offset includes both the sensor spectral offset

function and atmospherically scattered solar radiation contribution (see Equation 2).QUACsuse of the darkest non-zero image pixels for derivation of the background

spectrum can be problematic, since it is not always a physically valid estimate of thecommon offset. Most images contain artifacts, particularly near the image boundaries,

which can be among the darkest non-zero pixels in the image but which are anomalous

and physically unrepresentative of the image offset spectrum. Images also frequently

contain cloud shadows, which can similarly be among the darkest pixels in the image, butwhich exclude much of the atmospheric scattering component due to direct shading of the

densest part of the atmospheric column by the cloud. Still other images can be dominated

by bright materials having weak or missing shadows, so that the darkest pixels in thoseimages can be directly illuminated high reflectance materials that are substantially

brighter than the common offset spectrum component. Subtraction of the background

spectrum derived from the darkest non-zero image pixels can thus significantly under-

estimate or over-estimate the common offset contribution to pixel radiance. This canpotentially negatively impact the accuracy of QUACsderived gain spectrum and

resultant reflectance image. This was illustrated by Chakravortty and Chakrabarti (2011),

who reported a similar pattern of errors to those reported by Bitelli and Mandanici (2010)

in a QUAC-processed hyperspectral image containing numerous clouds and cloud

shadows of the Sunderlan Biosphere Reserve of West Bengal.

AAICsphysics-constrained approach largely avoids these sensitivities and

resultant errors. First, the method minimizes the chance that ACF(n) will be either an

over- or under-estimate of the offset through use of the simultaneous solution approach.

The approach derives offset, ACF(n), and gain, SCF(n), spectra that are mutuallycompatible and common to a majority of pixels across the image. This insures that

ACF(n) is minimally influenced by anomalies like cloud shadows and image artifacts,

and is physically realistic as an offset spectrum common to the image pixels, containingboth the relevant sensor function and atmospheric radiation transfer components. Second,

since the simultaneous solution approach works equally well with only two classes of


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materials as it does with a large diversity of materials, it minimizes sensitivity to both

land cover characteristics and diversity. As a result AAICwould be expected to be more

consistently accurate over a broader range of scene characteristics.

Discussion.

The findings here for AAICindicate that reflectance accuracies are generally comparableto those of the predictive physics-based models like FLASSH. Unlike the predictive

models, however, the results are not dependent on the analysts choice of input parameter

values. The image characteristics alone control AAICsoutcome, effectively eliminating

differences arising from variations in the analystsexperience levels and choices of inputparameters. The results are fully repeatable for an image, regardless of who does the

processing. As such, AAICmay provide an effective and reliable substitute for these

more complex interactive models. Shifting this time-consuming and often-problematic

atmospheric-correction step to the background and eliminating it from the analystsworkflow requirements can free the analyst to directly focus on the spectral analysis task

at hand. It can also potentially significantly decrease project turn-around time. Since thereis no required operator in the loop with AAICthe time to calibrate an image to units of

material reflectance is controlled primarily by hardware throughput. In contrast, the

predictive models require dedicated time for operator interactions and iterations, which

can often be a dominant component of the time to complete a project.

This ability to autonomously transform images to reflectance without the need forfield measurements or other user inputs could be of particular value to multi-temporal

classification and monitoring studies. The comparison of blue-tarp detection results in

Figure 14 illustrates this potential. Using the tarps as indicators of structural (roof)damage, and using the number from the earlier date as a baseline, the difference innumber of detected pixels could potentially be used to quantitatively estimate the areal

extent of roof damage in a sector of interest in the city. In this hypothetical illustration,

we selected the Ward 2C sector, for which there were 3594 detected pixels (32,844 m2)

of blue tarp material on the later date compared to 265 detected pixels (2,763 m2) on the

earlier date. Assuming some of the latter tarps to have been in place prior to landfall and

possibly unrelated to the storm, the difference in the number of detected pixels (3329

pixels, or 30,081 m2) between the two dates provides a conservative estimate of the

extent of structural damage inflicted by the storm in this sector of the city. This kind of

information, acquired soon after the event, can be of potentially high value to the

insurance industry to support early assessment and planning for spreading claims risk andmitigating cost, as well as to relevant governmental and non-governmental organizations

to support relief and recovery planning.

This capability could also be of potentially high value to multi-temporal

classification and monitoring studies such as the reported multi-temporal assessments of

changes in agricultural production (e.g., Gibson et al., 2012; Vintrou et al., 2012;), water


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